scholarly journals BioCPR–A Tool for Correlation Plots

Data ◽  
2021 ◽  
Vol 6 (9) ◽  
pp. 97
Author(s):  
Vidal Fey ◽  
Dhanaprakash Jambulingam ◽  
Henri Sara ◽  
Samuel Heron ◽  
Csilla Sipeky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Genetic Information ◽  
Dna Bases ◽  
Visualization Tool ◽  
Web Browser ◽  
Daunting Task ◽  
Powerful Approach ◽  
R Shiny ◽  
Strength Of Association

A gene is a sequence of DNA bases through which genetic information is passed on to the next generation. Most genes encode for proteins that ultimately control cellular function. Understanding the interrelation between genes without the application of statistical methods can be a daunting task. Correlation analysis is a powerful approach to determine the strength of association between two variables (e.g., gene-wise expression). Moreover, it becomes essential to visualize this data to establish patterns and derive insight. The most common method for gene expression visualization is to use correlation heatmaps in which the colors of the plot represent strength of co-expression. In order to address this requirement, we developed a visualization tool called BioCPR: Biological Correlation Plots in R. This tool performs both correlation analysis and subsequent visualization in the form of an interactive heatmap, improving both usability and interpretation of the data. BioCPR is an R Shiny-based application and can be run locally in Rstudio or a web browser.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

scholarly journals Testicular Melatonin and Its Pathway in Roe Deer Bucks (Capreolus capreolus) during Pre- and Post-Rut Periods: Correlation with Testicular Involution

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1874
Author(s):  
Alberto Elmi ◽  
Nadia Govoni ◽  
Augusta Zannoni ◽  
Martina Bertocchi ◽  
Chiara Bernardini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Roe Deer ◽  
Capreolus Capreolus ◽  
Reproductive Activity ◽  
Melatonin Receptors ◽  
Local Synthesis ◽  
Testicular Weight ◽  
And Function ◽  
Gene Expression Levels

Roe deer are seasonal breeders with a complete yearly testicular cycle. The peak in reproductive activity is recorded during summer, the rutting period, with the highest levels of androgens and testicular weight. Melatonin plays a pivotal role in seasonal breeders by stimulating the hypothalamus–pituitary–gonads axis and acting locally; in different species, its synthesis within testes has been reported. The aim of this study was to evaluate the physiological melatonin pattern within roe deer testes by comparing data obtained from animals sampled during pre- and post-rut periods. Melatonin was quantified in testicular parenchyma, along with the genetic expression of enzymes involved in its local synthesis (AANAT and ASMT) and function (UCP1). Melatonin receptors, MT1-2, were quantified both at protein and gene expression levels. Finally, to assess changes in reproductive hormonal profiles, testicular dehydroepiandrosterone (DHEA) was quantified and used for a correlation analysis. Melatonin and AANAT were detected in all samples, without significant differences between pre- and post-rut periods. Despite DHEA levels confirming testicular involution during the post-rut period, no correlations appeared between such involution and melatonin pathways. This study represents the first report regarding melatonin synthesis in roe deer testes, opening the way for future prospective studies in the physiology of this species.

scholarly journals Collaborative Cross Founder Expression Analysis (CCFEA)

2021 ◽  
Author(s):  
Richard R Green ◽  
Renee C Ireton ◽  
Martin Ferris ◽  
Kathleen Muenzen ◽  
David R Crosslin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Gene Expression Data ◽  
Viral Infections ◽  
Collaborative Cross ◽  
Visualization Tool ◽  
Expression Data ◽  
Individual Gene ◽  
Rnaseq Data

To understand the role of genetic variation in SARS and Influenza infections we developed CCFEA, a shiny visualization tool using public RNAseq data from the collaborative cross (CC) founder strains (A/J, C57BL/6J, 129s1/SvImJ, NOD/ShILtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Individual gene expression data is displayed across founders, viral infections and days post infection.

scholarly journals PredicTF: a tool to predict bacterial transcription factors in complex microbial communities

2021 ◽  
Author(s):  
Lummy Maria Oliveira Monteiro ◽  
Joao Saraiva ◽  
Rodolfo Brizola Toscan ◽  
Peter F Stadler ◽  
Rafael Silva-Rocha ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Microbial Communities ◽  
Open Source ◽  
Genetic Information ◽  
Environmental Isolate ◽  
Ammonium Oxidation ◽  
Cellular Gene ◽  
Bacterial Transcription

AbstractTranscription Factors (TFs) are proteins that control the flow of genetic information by regulating cellular gene expression. Here we describe PredicTF, a first platform supporting the prediction and classification of novel bacterial TF in complex microbial communities. We evaluated PredicTF using a two-step approach. First, we tested PredictTF’s ability to predict TFs for the genome of an environmental isolate. In the second evaluation step, PredicTF was used to predict TFs in a metagenome and 11 metatranscriptomes recovered from a community performing anaerobic ammonium oxidation (anammox) in a bioreactor. PredicTF is open source pipeline available at https://github.com/mdsufz/PredicTF.

scholarly journals John Arthur Joseph Pateman. 18 May 1926—18 May 2011

2018 ◽  
Vol 65 ◽  
pp. 299-315
Author(s):  
Claudio Scazzocchio
Keyword(s):  
Gene Expression ◽  
Protein Structure ◽  
Genetic Information ◽  
Positive Control ◽  
Control Of Gene Expression ◽  
Academic Background ◽  
Eukaryotic Microorganisms ◽  
The Subject ◽  
Relationship Of ◽  
The Relationship

John Pateman was a distinguished British microbial geneticist. He came from a working-class area of London and a non-academic background. His earliest contribution to genetics was the discovery (together with John Fincham) of intracistronic complementation, an important phenomenon to understand the relationship of genetic information and protein structure; this at a time when the actual coding relationships of DNA and proteins were not yet worked out. Later on, he and his students made a fundamental contribution to the understanding of control of gene expression in eukaryotic microorganisms, providing some of the earliest examples of positive control. This work also led to the discovery of a new enzyme cofactor, the only one discovered through purely genetic evidence. He worked at various universities in Britain and Australia and trained a number of students who further developed the subject.

scholarly journals Fidelity in Archaeal Information Processing

Archaea ◽  
2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Bart de Koning ◽  
Fabian Blombach ◽  
Stan J. J. Brouns ◽  
John van der Oost
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
Information Processing ◽  
Genome Size ◽  
Genetic Information ◽  
Important Determinant ◽  
Current Knowledge ◽  
The Other ◽  
Living Systems ◽  
Evolution Of Life

A key element during the flow of genetic information in living systems is fidelity. The accuracy of DNA replication influences the genome size as well as the rate of genome evolution. The large amount of energy invested in gene expression implies that fidelity plays a major role in fitness. On the other hand, an increase in fidelity generally coincides with a decrease in velocity. Hence, an important determinant of the evolution of life has been the establishment of a delicate balance between fidelity and variability. This paper reviews the current knowledge on quality control in archaeal information processing. While the majority of these processes are homologous in Archaea, Bacteria, and Eukaryotes, examples are provided of nonorthologous factors and processes operating in the archaeal domain. In some instances, evidence for the existence of certain fidelity mechanisms has been provided, but the factors involved still remain to be identified.

scholarly journals Gene expression signature: a powerful approach for drug discovery in diabetes

2017 ◽  
Vol 232 (2) ◽  
pp. R131-R139 ◽  
Author(s):  
Smithamol Sithara ◽  
Tamsyn M Crowley ◽  
Ken Walder ◽  
Kathryn Aston-Mourney
Keyword(s):  
Gene Expression ◽  
Complex Disease ◽  
Gene Expression Signature ◽  
New Drugs ◽  
Disease Symptoms ◽  
Current State ◽  
Powerful Approach ◽  
Underlying Causes ◽  
Underlying Pathophysiology

Type 2 diabetes (T2D) is increasing in prevalence at an alarming rate around the world. Much effort has gone into the discovery and design of antidiabetic drugs; however, those already available are unable to combat the underlying causes of the disease and instead only moderate the symptoms. The reason for this is that T2D is a complex disease, and attempts to target one biological pathway are insufficient to combat the full extent of the disease. Additionally, the underlying pathophysiology of this disease is yet to be fully elucidated making it difficult to design drugs that target the mechanisms involved. Therefore, the approach of designing new drugs aimed at a specific molecular target is not optimal and a more expansive, unbiased approach is required. In this review, we will look at the current state of diabetes treatments and how these target the disease symptoms but are unable to combat the underlying causes. We will also review how the technique of gene expression signatures (GESs) has been used successfully for other complex diseases and how this may be applied as a powerful tool for the discovery of new drugs for T2D.

scholarly journals Transcriptional Differences for COVID-19 Disease Map Genes between Males and Females Indicate a Different Basal Immunophenotype Relevant to the Disease

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1447
Author(s):  
Tianyuan Liu ◽  
Leandro Balzano-Nogueira ◽  
Ana Lleo ◽  
Ana Conesa
Keyword(s):  
Gene Expression ◽  
Disease Incidence ◽  
Tissue Expression ◽  
Matrix Metalloproteases ◽  
Sex And Gender ◽  
Demographic Groups ◽  
Shiny App ◽  
R Shiny ◽  
And Gender ◽  
High Level

Worldwide COVID-19 epidemiology data indicate differences in disease incidence amongst sex and gender demographic groups. Specifically, male patients are at a higher death risk than female patients, and the older population is significantly more affected than young individuals. Whether this difference is a consequence of a pre-existing differential response to the virus, has not been studied in detail. We created DeCovid, an R shiny app that combines gene expression (GE) data of different human tissue from the Genotype-Tissue Expression (GTEx) project along with the COVID-19 Disease Map and COVID-19 related pathways gene collections to explore basal GE differences across healthy demographic groups. We used this app to study differential gene expression of COVID-19 associated genes in different age and sex groups. We identified that healthy women show higher expression-levels of interferon genes. Conversely, healthy men exhibit higher levels of proinflammatory cytokines. Additionally, young people present a stronger complement system and maintain a high level of matrix metalloproteases than older adults. Our data suggest the existence of different basal immunophenotypes amongst different demographic groups, which are relevant to COVID-19 progression and may contribute to explaining sex and age biases in disease severity. The DeCovid app is an effective and easy to use tool for exploring the GE levels relevant to COVID-19 across demographic groups and tissues.

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